Qt and pyton GUI to measure statistics on SEM images(image files, bundled with the .ssc meta data from ZEISS SEMs) of circular objects.

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run the main app, app.py with :

python /qt_dot_fitter/app.py ./path_to_/a_reference_image.bmp ./root_foolder_with pictures min_size format

where min_size is a gray number that limits the smallest possible are fitted by the app; there is no general rule, it must be found out iteratively.

format is the format of the images on the hard disk.

there is an hidden last argument if working remotely and want to draw the GUI remotely and not over tunneled X11.

The output of the fitted measurements is saved on the disk as .csv file for every image. oUse the helper functions in stat.py to parse the results and produce meaningful plots.

Main GUI when opening the image  Image is labeled connecting regions of an integer array. After a intensity threshold has been selected with the slider to separate the contribution from the substrates and the geometrical dots (brighter ). Two pixels are connected when they are neighbors and have the same value. They can be neighbors either in a 4- or 8-connected sense:

4-connectivity      8-connectivity

     [ ]           [ ]  [ ]  [ ]
      |               \  |  /
[ ]--[ ]--[ ]      [ ]--[ ]--[ ]
      |               /  |  \
     [ ]           [ ]  [ ]  [ ]

As soon as the slider is moved the right panel will update with the labeled image to help choosing the correct treshold.


To close the program hit close, to start the fitting procedure press continue. Ellipses are fitted to the features to account for stigmation while writing and taking the images. The results of the fitting is plotted live in an external window, by superimposing a circle with a radius which is the average of the ellipses radii.